{"title":"使用机器学习算法预测糖尿病的比较分析","authors":"Ms. Madhuvanthi B, Dr. Baskaran T S","doi":"10.53555/jaz.v45is4.4308","DOIUrl":null,"url":null,"abstract":"Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage","PeriodicalId":509303,"journal":{"name":"Journal of Advanced Zoology","volume":"68 S24","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms\",\"authors\":\"Ms. Madhuvanthi B, Dr. Baskaran T S\",\"doi\":\"10.53555/jaz.v45is4.4308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage\",\"PeriodicalId\":509303,\"journal\":{\"name\":\"Journal of Advanced Zoology\",\"volume\":\"68 S24\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Zoology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.53555/jaz.v45is4.4308\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Zoology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53555/jaz.v45is4.4308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
糖尿病(DM)是一个严重的世界性健康问题,其发病率正在迅速增长。它是由血糖水平持续升高引起的一系列代谢性疾病。未确诊的糖尿病会导致各种问题,包括视网膜病变、肾病、神经病变和其他血管异常。在这种情况下,机器学习(ML)技术可能主要用于疾病的早期识别、诊断和治疗监测。本研究的核心思想是检测出预测疾病的强 ML 算法。为此,本研究选择了多种 ML 算法,即支持向量机 (SVM)、奈夫贝叶斯 (NB)、K 近邻 (KNN)、随机森林 (RF)、逻辑回归 (LR) 和决策树 (DT)。研究使用了皮马印度糖尿病(PID)和德国糖尿病两个数据集,并使用怀卡托知识分析环境(WEKA)3.8.6 工具实施了研究。该研究讨论了两个数据集的性能矩阵和分类器的错误率。结果显示,对于 PID 数据库 (PIDD),SVM 的准确率提高了 74%,而对于德国 RF 和 KNN,准确率提高了 98.7%。这项研究有助于医疗机构和研究人员了解 ML 算法在糖尿病初期预测中的价值和应用。
Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms
Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage